Gahow Wang
24663ebd35
Add tests for trend rider (integration, robustness, v4), US combo sweep, and US fundamentals modules.
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
import unittest
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import pandas as pd
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class USFundamentalTransformsTests(unittest.TestCase):
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def test_build_quarterly_factor_pack_derives_expected_signals(self):
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from research.us_fundamentals import build_quarterly_factor_pack
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quarter_ends = pd.to_datetime(
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["2023-03-31", "2023-06-30", "2023-09-30", "2023-12-31", "2024-03-31"]
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)
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close = pd.DataFrame(
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{"AAA": [100.0, 105.0], "BBB": [50.0, 48.0]},
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index=pd.to_datetime(["2024-06-03", "2024-06-04"]),
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)
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quarterly = {
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"net_income": pd.DataFrame(
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{"AAA": [10.0, 11.0, 12.0, 13.0, 14.0], "BBB": [4.0, 4.0, 5.0, 5.0, 5.0]},
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index=quarter_ends,
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),
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"gross_profit": pd.DataFrame(
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{"AAA": [30.0, 31.0, 32.0, 33.0, 34.0], "BBB": [10.0, 10.0, 11.0, 11.0, 11.0]},
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index=quarter_ends,
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),
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"equity": pd.DataFrame(
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{"AAA": [200.0, 205.0, 210.0, 215.0, 220.0], "BBB": [80.0, 81.0, 82.0, 83.0, 84.0]},
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index=quarter_ends,
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),
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"assets": pd.DataFrame(
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{"AAA": [300.0, 305.0, 310.0, 315.0, 320.0], "BBB": [130.0, 131.0, 132.0, 133.0, 134.0]},
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index=quarter_ends,
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),
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"shares": pd.DataFrame(
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{"AAA": [10.0, 10.0, 10.0, 10.0, 10.0], "BBB": [10.0, 10.0, 11.0, 11.0, 11.0]},
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index=quarter_ends,
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),
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}
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factor_pack = build_quarterly_factor_pack(quarterly, close, lag_days=60)
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self.assertIn("composite", factor_pack)
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self.assertIn("book_to_market", factor_pack)
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self.assertEqual(list(factor_pack["composite"].columns), ["AAA", "BBB"])
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self.assertGreater(
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float(factor_pack["composite"].iloc[-1]["AAA"]),
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float(factor_pack["composite"].iloc[-1]["BBB"]),
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)
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if __name__ == "__main__":
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unittest.main()
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